Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

7 October 2020

Papers citing "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples"

50 / 79 papers shown

Title
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness Emanuele Ballarin A. Ansuini Luca Bortolussi AAML 98 0 0 20 Feb 2025
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework Ping Guo Cheng Gong Xi Lin Fei Liu Zhichao Lu Qingfu Zhang Zhenkun Wang AAML 60 0 0 13 Jan 2025
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Yong Xie Weijie Zheng Hanxun Huang Guangnan Ye Xingjun Ma AAML 101 1 0 20 Nov 2024
Artificial Kuramoto Oscillatory Neurons Takeru Miyato Sindy Löwe Andreas Geiger Max Welling AI4CE 136 7 0 17 Oct 2024
Towards Universal Certified Robustness with Multi-Norm Training Enyi Jiang Gagandeep Singh Gagandeep Singh AAML 88 1 0 03 Oct 2024
ADBM: Adversarial diffusion bridge model for reliable adversarial purification Xiao-Li Li Wenxuan Sun Huanran Chen Qiongxiu Li Yining Liu Yingzhe He Jie Shi Xiaolin Hu AAML 99 10 0 01 Aug 2024
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers Haoyang Liu Aditya Singh Yijiang Li Haohan Wang AAML ViT 72 1 0 15 Mar 2024
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 47 265 0 01 Oct 2020
Understanding and Improving Fast Adversarial Training Maksym Andriushchenko Nicolas Flammarion AAML 50 286 0 06 Jul 2020
Smooth Adversarial Training Cihang Xie Mingxing Tan Boqing Gong Alan Yuille Quoc V. Le OOD 54 152 0 25 Jun 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 138 101 0 20 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 187 1,821 0 03 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 71 794 0 26 Feb 2020
Self-Adaptive Training: beyond Empirical Risk Minimization Lang Huang Chaoning Zhang Hongyang R. Zhang NoLa 41 202 0 24 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 49 155 0 20 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 208 18,607 0 13 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 120 1,167 0 12 Jan 2020
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Dan Hendrycks Norman Mu E. D. Cubuk Barret Zoph Justin Gilmer Balaji Lakshminarayanan OOD UQCV 88 1,293 0 05 Dec 2019
Towards Robust Image Classification Using Sequential Attention Models Daniel Zoran Mike Chrzanowski Po-Sen Huang Sven Gowal Alex Mott Pushmeet Kohli AAML 32 62 0 04 Dec 2019
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 56 977 0 29 Nov 2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Colin Raffel Noam M. Shazeer Adam Roberts Katherine Lee Sharan Narang Michael Matena Yanqi Zhou Wei Li Peter J. Liu AIMat 260 19,824 0 23 Oct 2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing Sven Gowal J. Uesato Chongli Qin Po-Sen Huang Timothy A. Mann Pushmeet Kohli AAML 78 89 0 21 Oct 2019
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets Yogesh Balaji Tom Goldstein Judy Hoffman AAML 154 103 0 17 Oct 2019
RandAugment: Practical automated data augmentation with a reduced search space E. D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le MQ 183 3,458 0 30 Sep 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 57 230 0 24 Jul 2019
Adversarial Robustness through Local Linearization Chongli Qin James Martens Sven Gowal Dilip Krishnan Krishnamurthy Dvijotham Alhussein Fawzi Soham De Robert Stanforth Pushmeet Kohli AAML 56 307 0 04 Jul 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 51 346 0 14 Jun 2019
Intriguing properties of adversarial training at scale Cihang Xie Alan Yuille AAML 42 68 0 10 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 57 544 0 09 Jun 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He John E. Hopcroft Liwei Wang 59 156 0 03 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 96 752 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 52 333 0 31 May 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 87 17,950 0 28 May 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi S. Maeda Masanori Koyama Takeru Miyato OOD 62 130 0 24 May 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 63 899 0 18 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 96 2,018 0 08 Feb 2019
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples? Nicholas Carlini SILM 37 35 0 06 Feb 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 62 726 0 28 Jan 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang Kun Xu Chao Du Ning Chen Jun Zhu AAML 57 436 0 25 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 94 2,525 0 24 Jan 2019
Feature Denoising for Improving Adversarial Robustness Cihang Xie Yuxin Wu Laurens van der Maaten Alan Yuille Kaiming He 76 907 0 09 Dec 2018
Robustness via curvature regularization, and vice versa Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi J. Uesato P. Frossard AAML 58 319 0 23 Nov 2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models Sven Gowal Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel Chongli Qin J. Uesato Relja Arandjelović Timothy A. Mann Pushmeet Kohli AAML 56 551 0 30 Oct 2018
Logit Pairing Methods Can Fool Gradient-Based Attacks Marius Mosbach Maksym Andriushchenko T. A. Trost Matthias Hein Dietrich Klakow AAML 53 82 0 29 Oct 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah Aleksander Madry AAML OOD 28 200 0 09 Sep 2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing Logan Engstrom Andrew Ilyas Anish Athalye AAML 50 141 0 26 Jul 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 57 447 0 31 May 2018
AutoAugment: Learning Augmentation Policies from Data E. D. Cubuk Barret Zoph Dandelion Mané Vijay Vasudevan Quoc V. Le 93 1,764 0 24 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 114 786 0 30 Apr 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 70 627 0 16 Mar 2018